Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
arXiv:2310.07895v2 Announce Type: replace Abstract: This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of $98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. This allows for precise localization wi
The continuous advancements in AI, particularly in fields like medical imaging and time-series analysis, are making sophisticated diagnostic tools more feasible and accurate, driving this development.
This development represents a significant step towards more precise and automated medical diagnostics, leveraging AI to improve existing healthcare technologies and potentially reduce human error.
The ability to accurately localize within the GI tract using a combination of CNNs and HMMs significantly enhances the diagnostic capability of Video Capsule Endoscopy, improving patient outcomes and efficiency.
- · Medical AI companies
- · Healthcare providers
- · Patients with GI conditions
- · Medical device manufacturers
- · Manual image reviewers
- · Traditional diagnostic methods
Improved early detection rates for gastrointestinal diseases.
Reduced healthcare costs due to more efficient and accurate diagnostic processes.
Expansion of AI-driven diagnostic tools to other internal body examinations, leading to a broader revolution in non-invasive medical imaging analysis.
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Read at arXiv cs.LG